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boost verify

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serengil 2020-11-30 16:50:49 +03:00
parent 4535e37c52
commit 45f17417af
3 changed files with 190 additions and 196 deletions
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189
deepface/DeepFace.py
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@ -4,13 +4,12 @@ warnings.filterwarnings("ignore")
import time import time
import os import os
from os import path from os import path
from pathlib import Path
import numpy as np import numpy as np
import pandas as pd import pandas as pd
from tqdm import tqdm from tqdm import tqdm
import pickle import pickle
from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace, DeepID, DlibWrapper from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace, DeepID, DlibWrapper, Boosting
from deepface.extendedmodels import Age, Gender, Race, Emotion from deepface.extendedmodels import Age, Gender, Race, Emotion
from deepface.commons import functions, realtime, distance as dst from deepface.commons import functions, realtime, distance as dst
@ -48,150 +47,19 @@ def verify(img1_path, img2_path = '', model_name = 'VGG-Face', distance_metric =
resp_objects = [] resp_objects = []
if model_name == 'Ensemble': #--------------------------------
print("Ensemble learning enabled")
import lightgbm as lgb #lightgbm==2.3.1
if model == None:
model = {}
model_pbar = tqdm(range(0, 4), desc='Face recognition models')
for index in model_pbar:
if index == 0:
model_pbar.set_description("Loading VGG-Face")
model["VGG-Face"] = build_model('VGG-Face')
elif index == 1:
model_pbar.set_description("Loading Google FaceNet")
model["Facenet"] = build_model('Facenet')
elif index == 2:
model_pbar.set_description("Loading OpenFace")
model["OpenFace"] = build_model('OpenFace')
elif index == 3:
model_pbar.set_description("Loading Facebook DeepFace")
model["DeepFace"] = build_model('DeepFace')
#--------------------------
#validate model dictionary because it might be passed from input as pre-trained
found_models = []
for key, value in model.items():
found_models.append(key)
if ('VGG-Face' in found_models) and ('Facenet' in found_models) and ('OpenFace' in found_models) and ('DeepFace' in found_models):
print("Ensemble learning will be applied for ", found_models," models")
else:
raise ValueError("You would like to apply ensemble learning and pass pre-built models but models must contain [VGG-Face, Facenet, OpenFace, DeepFace] but you passed "+found_models)
#--------------------------
model_names = ["VGG-Face", "Facenet", "OpenFace", "DeepFace"]
metrics = ["cosine", "euclidean", "euclidean_l2"]
pbar = tqdm(range(0,len(img_list)), desc='Verification')
#for instance in img_list:
for index in pbar:
instance = img_list[index]
if type(instance) == list and len(instance) >= 2:
img1_path = instance[0]
img2_path = instance[1]
ensemble_features = []; ensemble_features_string = "["
for i in model_names:
custom_model = model[i]
#input_shape = custom_model.layers[0].input_shape[1:3] #my environment returns (None, 224, 224, 3) but some people mentioned that they got [(None, 224, 224, 3)]. I think this is because of version issue.
input_shape = custom_model.layers[0].input_shape if model_name == 'Ensemble':
return Boosting.verify(model = model, img_list = img_list, bulkProcess = bulkProcess, enforce_detection = enforce_detection, detector_backend = detector_backend)
if type(input_shape) == list:
input_shape = input_shape[0][1:3]
else:
input_shape = input_shape[1:3]
img1 = functions.preprocess_face(img = img1_path, target_size = input_shape, enforce_detection = enforce_detection, detector_backend = detector_backend)
img2 = functions.preprocess_face(img = img2_path, target_size = input_shape, enforce_detection = enforce_detection, detector_backend = detector_backend)
img1_representation = custom_model.predict(img1)[0,:]
img2_representation = custom_model.predict(img2)[0,:]
for j in metrics:
if j == 'cosine':
distance = dst.findCosineDistance(img1_representation, img2_representation)
elif j == 'euclidean':
distance = dst.findEuclideanDistance(img1_representation, img2_representation)
elif j == 'euclidean_l2':
distance = dst.findEuclideanDistance(dst.l2_normalize(img1_representation), dst.l2_normalize(img2_representation))
if i == 'OpenFace' and j == 'euclidean': #this returns same with OpenFace - euclidean_l2
continue
else:
ensemble_features.append(distance)
if len(ensemble_features) > 1:
ensemble_features_string += ", "
ensemble_features_string += str(distance)
#print("ensemble_features: ", ensemble_features)
ensemble_features_string += "]"
#-------------------------------
deepface_ensemble = functions.boosting_method()
#---------------------------
prediction = deepface_ensemble.predict(np.expand_dims(np.array(ensemble_features), axis=0))[0]
verified = np.argmax(prediction) == 1
score = prediction[np.argmax(prediction)]
#print("verified: ", verified,", score: ", score)
resp_obj = {
"verified": verified
, "score": score
, "distance": ensemble_features_string
, "model": ["VGG-Face", "Facenet", "OpenFace", "DeepFace"]
, "similarity_metric": ["cosine", "euclidean", "euclidean_l2"]
}
if bulkProcess == True:
resp_objects.append(resp_obj)
else:
return resp_obj
#-------------------------------
if bulkProcess == True:
resp_obj = {}
for i in range(0, len(resp_objects)):
resp_item = resp_objects[i]
resp_obj["pair_%d" % (i+1)] = resp_item
return resp_obj
return None
#ensemble learning block end #ensemble learning block end
#-------------------------------- #--------------------------------
#ensemble learning disabled #ensemble learning disabled
if model == None: if model == None:
model = build_model(model_name) model = build_model(model_name)
"""else: #model != None
print("Already built model is passed")"""
#------------------------------ #------------------------------
#face recognition models have different size of inputs #face recognition models have different size of inputs
#my environment returns (None, 224, 224, 3) but some people mentioned that they got [(None, 224, 224, 3)]. I think this is because of version issue. #my environment returns (None, 224, 224, 3) but some people mentioned that they got [(None, 224, 224, 3)]. I think this is because of version issue.
@ -458,28 +326,8 @@ def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine',
if model == None: if model == None:
if model_name == 'Ensemble': if model_name == 'Ensemble':
print("Ensemble learning enabled") print("Ensemble learning enabled")
#TODO: include DeepID in ensemble method models = Boosting.loadModel()
import lightgbm as lgb #lightgbm==2.3.1
models = {}
pbar = tqdm(range(0, len(model_names)), desc='Face recognition models')
for index in pbar:
if index == 0:
pbar.set_description("Loading VGG-Face")
models['VGG-Face'] = build_model('VGG-Face')
elif index == 1:
pbar.set_description("Loading FaceNet")
models['Facenet'] = build_model('Facenet')
elif index == 2:
pbar.set_description("Loading OpenFace")
models['OpenFace'] = build_model('OpenFace')
elif index == 3:
pbar.set_description("Loading DeepFace")
models['DeepFace'] = build_model('DeepFace')
else: #model is not ensemble else: #model is not ensemble
model = build_model(model_name) model = build_model(model_name)
@ -488,24 +336,10 @@ def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine',
print("Already built model is passed") print("Already built model is passed")
if model_name == 'Ensemble': if model_name == 'Ensemble':
import lightgbm as lgb #lightgbm==2.3.1
#validate model dictionary because it might be passed from input as pre-trained
found_models = []
for key, value in model.items():
found_models.append(key)
if ('VGG-Face' in found_models) and ('Facenet' in found_models) and ('OpenFace' in found_models) and ('DeepFace' in found_models):
print("Ensemble learning will be applied for ", found_models," models")
else:
raise ValueError("You would like to apply ensemble learning and pass pre-built models but models must contain [VGG-Face, Facenet, OpenFace, DeepFace] but you passed "+found_models)
Boosting.validate_model(model)
models = model.copy() models = model.copy()
#threshold = functions.findThreshold(model_name, distance_metric)
#--------------------------------------- #---------------------------------------
file_name = "representations_%s.pkl" % (model_name) file_name = "representations_%s.pkl" % (model_name)
@ -666,7 +500,7 @@ def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine',
#---------------------------------- #----------------------------------
#lightgbm model #lightgbm model
deepface_ensemble = functions.boosting_method() deepface_ensemble = Boosting.build_gbm()
y = deepface_ensemble.predict(x) y = deepface_ensemble.predict(x)
@ -747,7 +581,7 @@ def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine',
raise ValueError("Passed db_path does not exist!") raise ValueError("Passed db_path does not exist!")
return None return None
def stream(db_path = '', model_name ='VGG-Face', distance_metric = 'cosine', enable_face_analysis = True): def stream(db_path = '', model_name ='VGG-Face', distance_metric = 'cosine', enable_face_analysis = True):
functions.initialize_detector(detector_backend = 'opencv') functions.initialize_detector(detector_backend = 'opencv')
@ -790,4 +624,3 @@ def initialize_input(img1_path, img2_path = None):
#main #main
functions.initializeFolder() functions.initializeFolder()

179
deepface/basemodels/Boosting.py Normal file
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@ -0,0 +1,179 @@
from deepface import DeepFace
from tqdm import tqdm
import os
from os import path
from pathlib import Path
import numpy as np
import lightgbm as lgb #lightgbm==2.3.1
from deepface.commons import functions, distance as dst
def loadModel():
model = {}
model_pbar = tqdm(range(0, 4), desc='Face recognition models')
for index in model_pbar:
if index == 0:
model_pbar.set_description("Loading VGG-Face")
model["VGG-Face"] = DeepFace.build_model('VGG-Face')
elif index == 1:
model_pbar.set_description("Loading Google FaceNet")
model["Facenet"] = DeepFace.build_model('Facenet')
elif index == 2:
model_pbar.set_description("Loading OpenFace")
model["OpenFace"] = DeepFace.build_model('OpenFace')
elif index == 3:
model_pbar.set_description("Loading Facebook DeepFace")
model["DeepFace"] = DeepFace.build_model('DeepFace')
return model
def validate_model(model):
#validate model dictionary because it might be passed from input as pre-trained
found_models = []
for key, value in model.items():
found_models.append(key)
if ('VGG-Face' in found_models) and ('Facenet' in found_models) and ('OpenFace' in found_models) and ('DeepFace' in found_models):
print("Ensemble learning will be applied for ", found_models," models")
else:
raise ValueError("You would like to apply ensemble learning and pass pre-built models but models must contain [VGG-Face, Facenet, OpenFace, DeepFace] but you passed "+found_models)
def build_gbm():
home = str(Path.home())
if os.path.isfile(home+'/.deepface/weights/face-recognition-ensemble-model.txt') != True:
print("face-recognition-ensemble-model.txt will be downloaded...")
url = 'https://raw.githubusercontent.com/serengil/deepface/master/deepface/models/face-recognition-ensemble-model.txt'
output = home+'/.deepface/weights/face-recognition-ensemble-model.txt'
gdown.download(url, output, quiet=False)
ensemble_model_path = home+'/.deepface/weights/face-recognition-ensemble-model.txt'
deepface_ensemble = lgb.Booster(model_file = ensemble_model_path)
return deepface_ensemble
def verify(model, img_list, bulkProcess, enforce_detection, detector_backend):
print("Ensemble learning enabled")
if model == None:
model = loadModel()
validate_model(model)
#--------------------------
model_names = ["VGG-Face", "Facenet", "OpenFace", "DeepFace"]
metrics = ["cosine", "euclidean", "euclidean_l2"]
resp_objects = []
#--------------------------
if model == None:
model = loadModel()
#--------------------------
validate_model(model)
#--------------------------
pbar = tqdm(range(0,len(img_list)), desc='Verification')
for index in pbar:
instance = img_list[index]
if type(instance) == list and len(instance) >= 2:
img1_path = instance[0]
img2_path = instance[1]
ensemble_features = []; ensemble_features_string = "["
for i in model_names:
custom_model = model[i]
input_shape = custom_model.layers[0].input_shape
if type(input_shape) == list:
input_shape = input_shape[0][1:3]
else:
input_shape = input_shape[1:3]
#----------------------------------
img1 = functions.preprocess_face(img = img1_path, target_size = input_shape
, enforce_detection = enforce_detection
, detector_backend = detector_backend)
img2 = functions.preprocess_face(img = img2_path, target_size = input_shape
, enforce_detection = enforce_detection
, detector_backend = detector_backend)
img1_representation = custom_model.predict(img1)[0,:]
img2_representation = custom_model.predict(img2)[0,:]
for j in metrics:
if j == 'cosine':
distance = dst.findCosineDistance(img1_representation, img2_representation)
elif j == 'euclidean':
distance = dst.findEuclideanDistance(img1_representation, img2_representation)
elif j == 'euclidean_l2':
distance = dst.findEuclideanDistance(dst.l2_normalize(img1_representation), dst.l2_normalize(img2_representation))
#------------------------
#this returns same with OpenFace - euclidean_l2
if i == 'OpenFace' and j == 'euclidean':
continue
else:
ensemble_features.append(distance)
if len(ensemble_features) > 1:
ensemble_features_string += ", "
ensemble_features_string += str(distance)
ensemble_features_string += "]"
#-------------------------------
deepface_ensemble = build_gbm()
#-------------------------------
prediction = deepface_ensemble.predict(np.expand_dims(np.array(ensemble_features), axis=0))[0]
verified = np.argmax(prediction) == 1
score = prediction[np.argmax(prediction)]
#print("verified: ", verified,", score: ", score)
resp_obj = {
"verified": verified
, "score": score
, "distance": ensemble_features_string
, "model": ["VGG-Face", "Facenet", "OpenFace", "DeepFace"]
, "similarity_metric": ["cosine", "euclidean", "euclidean_l2"]
}
if bulkProcess == True:
resp_objects.append(resp_obj)
else:
return resp_obj
if bulkProcess == True:
resp_obj = {}
for i in range(0, len(resp_objects)):
resp_item = resp_objects[i]
resp_obj["pair_%d" % (i+1)] = resp_item
return resp_obj

18
deepface/commons/functions.py
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@ -447,21 +447,3 @@ def preprocess_face(img, target_size=(224, 224), grayscale = False, enforce_dete
img_pixels /= 255 #normalize input in [0, 1] img_pixels /= 255 #normalize input in [0, 1]
return img_pixels return img_pixels
def boosting_method():
import lightgbm as lgb #lightgbm==2.3.1
home = str(Path.home())
if os.path.isfile(home+'/.deepface/weights/face-recognition-ensemble-model.txt') != True:
print("face-recognition-ensemble-model.txt will be downloaded...")
url = 'https://raw.githubusercontent.com/serengil/deepface/master/deepface/models/face-recognition-ensemble-model.txt'
output = home+'/.deepface/weights/face-recognition-ensemble-model.txt'
gdown.download(url, output, quiet=False)
ensemble_model_path = home+'/.deepface/weights/face-recognition-ensemble-model.txt'
deepface_ensemble = lgb.Booster(model_file = ensemble_model_path)
return deepface_ensemble